E-LEARNING IN THE SEMANTIC AGE

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21 Οκτ 2013 (πριν από 4 χρόνια και 17 μέρες)

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1


E
-
LEARNING IN THE SEMA
NTIC AGE

Matthias Palmér

Ambjörn Naeve

Mikael Nilsson

The KMR (Knowledge Management Research) group

CID (Centre for user oriented IT Design)

NADA (Numerical Analysis and Computing Science)

KTH (Royal Institute of Technology)

100
44 Stockholm, Sweden

[amb|mini|matthias@nada.kth.se]

ABSTRACT

Today educational technologies are reaching a state that allows interoperability and
reuse of learning resources. The underlying techniques rely heavily on the standards
movement for metadata re
presentation. On top of this, monolithic reference
platforms are being developed with the aim to ease application development.
However, we do not think this approach is flexible enough to embrace future
learning techniques. In contrast, we suggest a learni
ng framework where services can
be developed and exchanged between as well as within systems. A fundamental part
of this framework is the semantic layer, which builds on the structure of the Semantic
Web. Hence we do not regard metadata as something 'objec
tive' that you have to
download from some central server. On the contrary, metadata should be allowed to
consist of subjective views of resources that are distributed and shared in contexts
that can evolve dynamically. In support of such requirements, our
learning
framework consists of a combination of semantic web techniques and peer
-
to
-
peer
services for search, retrieval, publication, replication and mapping of metadata.

KEYWORDS

Learning Framework, Semantic Web, Conceptual Web, interoperability, peer
-
to
-
peer, concept browser, Conzilla, Edutella.



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1.

INTRODUCTION

Computer Based Instruction (CBI) and Intelligent Tutoring systems (ITS) are two
important areas within the field of IT
-
supported learning, which in recent years have
drifted somewhat apart. While CB
I has taken a more pragmatic engineering
approach, ITS has developed more into a field of advanced research. With the
success of the web, their respective agendas have come to focus on issues of
interoperability and reuse
[35]
.

This has been acknowledged by several key
international players including IEEE
[33]
, IMS
[34]
, ARIADNE
[37]

and AICC
[38]
,
resulting in a m
ultitude of standards that can be used for building interoperable
learning systems. Fortunately, many of these standards complement each other or
seem to be converging towards a common form, which allows applications to use
them together. In order to ease
this integration, several standards have been collected
and harmonized and often a reference platform has been given as proof of concept.
One example is SCORM
[35]

by ADL
[36]
, which provides a refe
rence model that
includes collected/developed standards for content aggregation as well as a run
-
time
environment. A more recent example is OKI
[46]
, which is a project initiated by
MIT
1

in order to provide all of their courses

online. They plan to deliver an
architecture and some APIs
2

for basic services. Furthermore, IMS provides a set of
basic standards but they also have an enterprise standard (message oriented
approach) which specifies how an LMS
3

should exchange messages r
egarding
management of courses and students, such as tracking, assessment etc. Blackboard
[48]

is an example of a commercial LMS that uses IMS enterprise.

These attempts at building learning platforms for interoperability are m
ainly
targeted to ease the need of LMSes for adaptation to standards, but as a consequence,
learners can be expected to gain more freedom. For example, the goal of SCORM is
to provide a reference model for content that is durable (survives system changes),

interoperable (between systems), accessible (indexed and searchable) and reusable
(able to be modified in different contexts and by different tools). This will hopefully
allow students to move more freely between LMSs and even to combine several
services
from different LMSs.

1.1.

Some problems with current approaches

We see at least three problems with current approaches. First, most providers of
content have large monolithic systems where adaptation to e.g. SCORM will not
significantly change the underlying te
acher
-
learner model. Students will be presented
with material in a (maybe personalized) context often leading up to some form of
(standardized) test. New and more interesting methods for learning
-

such as
techniques for collaboration, annotation, conceptu
al modeling etc.
-

will not profit
from such adaptation
4
.

Second, even though monolithic, closed or proprietary systems will be able to
exchange learning resources, course
-
like structures and keep track of students with
the help of those standards, they w
ill need to go through yet another process of
adaptation to the next big batch of agreements on learning technologies, such as e.g.
profiling and tracking of student performance.




1


Massachusetts Institute of Technology.

2


Application Programming Interfaces.

3


Learning Mana
gement System.

4

Our learner
-
centric educational architecture called a
Knowledge Manifold
, which supports inquiry
-
based
and customizable forms of learning is discussed in
[6]
,
[7]

as well as in a se
parate article
[9]

in these
proceedings.

E
-
LEARNING IN THE SEMA
NTIC AGE







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Third, the current perspective on metadata is too limited. Anyone who has
som
ething to say about a learning resource should be able to do so. This includes
learners, teachers and content contributors such as authors and providers.
Communicating this metadata is equally important as it can help, direct or encourage
others to activel
y participate and learn. Proposed solutions, such as
[35]
, will result
in learning resources (and their metadata) that will reappear in different versions and
formats rather than dynamically evolve and improve. Important synerg
y effects will
be missing.


1.2.

Proposed solutions

We will begin by discussing the third problem, and then, in response to the first two
problems, we will introduce the notion of a learning framework.

To share information such as SCORM metadata and CSFs
5

is si
mple, or at least
straightforward, when the standards are well designed and documented. However,
the techniques mentioned above will need resources to be identified and talked about
in a distributed way that is similar to what is done on the web
6
. In order

to enable
this, we need an extendable (machine
-
understandable) "semantic language" for
expressing the semantics of anything that is identifiable. To be extendable means
allowing new schemas to be dynamically added as well as allowing tools and
learners to

interact in new ways not yet conceived within a standards body. RDF
7

[39]

and the vision of the Semantic Web
[43]

provide a good starting point for such a
semantic language. RDF allows meaning, i.e
. semantics, to be expressed in a
distributed manner similar to how the web works. What the semantic web can do for
e
-
learning is discussed in section
3.2
.

We also need mechanisms that enable searching for, locating and exchang
ing
semantic information expressed in this language. These mechanisms, together with
the semantic language, constitute the basic ingredients of a learning framework.
SCORMs 'reference model' is a first step in this direction, since it describes how
content

should be described and exchanged in a general way. We think that a
learning framework should consist of layers that include a semantic (web) layer as
well as a service layer on top of this. Certainly more layers will be needed, e.g. a
communication layer

and an application layer. This will be briefly discussed in
section
2.3
.

A learning framework should support applications in working more flexibly
with learning technologies and should provide a platform similar to the Java pl
atform
enterprise edition. Hence, we think that a future LMS should consist of a set of
independent but cooperating non
-
monolithic services/applications that are available
in public pools. Applications/services can then be built on top of existing services

-

providing a better interface
-

or by defining new independent services. Some
examples of such applications/services developed by the KMR group at CID will be
presented in chapter
4
.







5


Content Structure Formats.

6


This

is beyond the current scope of SCORM
.

7


Resource Description Framework.

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PALMÉR, NAEVE, NILSS
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2.

INTRODUCING OUR LEAR
NING FRAMEWORK

Ther
e is a growing recognition for the need of a learning framework
8
, which captures
relevant standards in a layered fashion in order to allow for interoperability and
simplified application development. We will now present an alternative, which
allows a more
capable and flexible design based on techniques from the semantic
web community
9
.

2.1.

Supporting standards

The strength of standards lies in their capacity to set the stage for interoperability
between LMS:es as well as between applications within a system. Su
ch
interoperability can be achieved in several ways. Let us examine three different ways
to interoperate
10
:




Integration at the software level, with components talking directly to one another


using APIs.

• Message
-
based integration, with components ex
changing messages in plain text


using a protocol such as SOAP
11
.

• Data
-
based integration using XML files or records in relational databases to share


information.


The first alternative presents several problems. It is rather inflexible and risky,
since
it forces you to have a tight coupling between your system and services. The second
alternative, messaging, is better, since it allows more flexibility. However, the
semantics of the messages needs to be understood, at least in part, by all involved
parties. In some sense this common understanding relies on the third alternative, data
integration, which pre
-
supposes a common understanding of the schemas to use. The
basic standards, e.g. IMS metadata
[14]

and content packag
ing, are mainly concerned
with defining data models (schemas) and operate on the level of data integration.

In the Learning Framework presented here, we focus on this view and think of
standards as either schemas in isolation or schemas together with the s
ervices that
make use of them in order to accomplish different tasks. Messages between or inside
systems should be seen as transmitting facts about resources (content, courses,
students etc.) conforming to certain schemas that specific services have declar
ed an
interest in. So in this case the distinction between the message integration and the
data integration approach is only a matter of context, i.e. it depends on how a service
treats the information, e.g. like facts about resources, descriptions of proc
esses or a
piece of information in an ongoing dialogue between two services.

One example could be a student performing a search based on a query. If this
search is successful and she wants to save it for future use, what should be stored
-

the end results

or the initiating query? The results are certainly a set of facts and can
therefore without problems be stored as something similar to bookmarks. However,
if the query is stored as just another result in such a bookmark system, whenever it is
retrieved, i
t should be treated as a process.

In order to complicate the example, the student might want to have another
representation

of the successful query in her personal bookmarks environment. This



8


Sometimes called a 'platform' or a 'model'.

9


See
[1]
,
[11]

and
[43]
.

10

As described by Mark Norton in his speak at the IMS symposium in Ottawa in August 2001, reported by
Scott Wilson in
[20]
.

11

Simple Object Application Protocol.

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could be achieved by introducing a rule where the pre
-
condition

consists of the query
itself and the post
-
condition consists of the desired
representation

of the query. Here
we see something quite interesting, the query schema is reused in the rule schema,
which corresponds to piping the search services into the rule
matching service. This
illustrates a situation where services
-

capable of treating certain schemas
-

can reuse
other services in order to avoid duplication of work
12
. A reference implementation of
the Learning Framework should provide a minimal set of serv
ices supporting the
most common schemas.

2.2.

Layers and services

Computer science is constantly changing. Individual techniques and even complete
paradigms can become obsolete during a very short period of time. Naturally,
systems that have not been "designed
for change" will not survive very long. A
common approach to designing for change is to divide the system into layers, which
communicate only via well
-
defined interfaces. In a closed layered architecture each
layer can interface with only the immediate bor
dering layers, while in an open
layered architecture any layer can interface with all layers beneath it. This is very
similar to the design approach of computer networks
13
. We think that a learning
framework should be layered but with well
-
defined interface
s only for the lowest
(physical and transport) layers. The semantic layer, which comes next, should serve
as the "middle ground" for the different sub
-
systems in the service and applications
layers above.

In
[5]

there is a dis
cussion of how layered information models should
achieve interoperability between applications, and a special reference model,
Information Model Interoperability (IMI), is defined. This model contains a syntax
layer (serialization, e.g. XML/RDF), an object

layer (facts about resources) and a
semantic layer (the schemas used). In a knowledge representation system there is no
well
-
defined border between facts and schemas
14

[5]
. This is also noted in
[20]
.
Hence our semantic layer will actually embrace both the object layer and the
semantic layer defined in IMI. However, the distinction is still there, and the schemas
will probably be the preferable way for services to specify the domains within which
the
y work.

2.3.

Suggested layers

Figure 1 depicts some of the suggested layers in our Learning Framework
15
. We will
now perform a bottom up walk
-
through and discuss some of the techniques occurring
in each layer.





12

Reuse of schemas saves a lot of work as well
, which is beneficial for tool builders as well as for
standards bodies.

13

For an overview, see standard textbooks such as
[17]
.

14

In fact, the ULM (Unified Language Modeling) methodology that we have developed takes advantage
of this fact. See
[7]

and
[10]

for further details.

15

This is by no means a complete picture.

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PALMÉR, NAEVE, NILSS
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Figure 1.

Some suggested layers of our Learning Framework
.


Physical and Transport layer:
The bits have to be transported over some medium,
e.g. cable, infrared beam etc. On top of this there is a stack of protocols such as e.g.
TCP/IP.


Exchange layer:

For retrieving resources http is often enough. WebDAV
16

is a
n
extension, which allows intelligible write/access/versioning of the resources as well
as of their metadata. Peer
-
to
-
peer systems, such as JXTA
17

[42]
, allow decentralized
repositories, which lowers the threshold for publicatio
n of content and metadata.
Also database connectivity such as JDBC
18

fits nicely into this layer.


Semantic layer:

It is somewhat unclear to what extent this layer exists besides from
serving as a temporary store for models and as a middle
-
ground between s
ervices as
well as between services and the exchange layer. What is clear though is that the
semantic layer should be compatible with the Semantic Web
[43]

and hence use
RDF/RDFS
19

[39]
,
[40]
, and all possible extensions in terms of schemas, ontologies
etc.


Service layer:

Hopefully a multitude of services will soon be crafted in order to
solve problems such as profiling and resolving of resources. Several more advanced
se
rvices will need active cooperation from other peers in order to work properly, e.g.
search for metadata, definition of mappings between schemas and publication of
content/metadata. Such cooperation can take place via a peer
-
to
-
peer system such as
JXTA
20
. S
ince not all services and applications will be able to use RDF and schemas
to express themselves, there will always be a need for export to XML or special
APIs. Some services will deal with this explicitly, probably as part of a 'pipe' of



16

Web Distributed Authoring and Versioning.

17

Juxtapose.

18

Java Data Base Connection.

19

Resource Description Framework / Resource Description Framework Schema.

20

The KMR group at CID is
actively participating in an international collaborative JXTA
-
based project
called
Edutella

[3]
,
[31]
, where several such services will be developed, see section
4.2
. In Figure 1,
services that will be part of Edutella are drawn within the dashed box.

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services. A very
important example is provided by 'unintelligent' light
-
weight clients
that need to work with simpler representations of metadata.


Application layer:

This is the topmost layer where man and machine meet. Hence it
will require a multitude of different capab
ilities with user
-
friendly interfaces. See
chapter
4

for an introduction to some of our existing and future applications.
Probably there should be a sub
-
layer where highly reusable applications belong, e.g.
so
-
called plugins.


Framework main control:

This layer constitutes the main control structure where
all modules register their capabilities via some language such as WSDL
21

[47]

or
maybe OIL
22

[41]
. Typically an applica
tion registers which schemas, protocols, APIs
and/or mime
-
types that it is capable of exposing to the user via some UI
23
. If it has no
UI it is probably a service and not an application.

3.

SUPPORTING SEMANTICS

IN A LEARNING FRAMEW
ORK

The Learning Framework su
ggested above is open to a multitude of new services. In
order to be effective, it needs a powerful language for expressing facts about
resources and schemas that will allow machines as well as humans to understand
how these facts are related without relyi
ng on heuristics. Moreover, there is a need
for expressing facts about remote (identifiable) objects without accessing remote data
stores.

If the semantic web did not already exist, we would probably have to invent
something similar in order to be able to

realize our Learning Framework. But the
semantic web is more than just a necessary prerequisite for this framework. It allows
the definition of a large variety of innovative services that are not supported by the
structure of the present web. In the next
sections we will give a brief introduction to
the Semantic Web and its capabilities within a learning context.

3.1.

The Semantic Web

The present web is a highly intertwined graph, constructed mainly for human
consumption, where heuristics are needed in order to

digest the meaning (semantics)
of a resource. Hence the construction of agents that could help to automatically sort
out information is hard. This does not mean that the existing web lacks semantics.
However, it is presently not accessible in a standardiz
ed machine
-
readable way.

The
Semantic Web

is a W3C initiative
[43]

that will increase the power of the
existing web because it will adhere to the basic principles of the latter: "anyone can
say anything about anything"
[1]
. The emerging Semantic Web will not replace the
existing web. Instead it will become a complement that will allow the creation of
more powerful tools for searching and making inferences
24
.

The current web consists of resources (oft
en documents) that are interconnected
via their identifiers (URIs
25
). The Semantic Web will add semantics to these
resources by adorning them with
properties

expressed in RDF/RDFS
[39]
,
[40]
. A
prope
rty is a
triple

that consists of
subject
,
object

and
predicate
. The subject is the



21

Web Services Description Language.

22

Ontology Inference Layer.

23

User Interface.

24

See
[1]

for an excellent article on the promises of the Semantic Web.

25

Uniform (or Universal) Resource Identifier.

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PALMÉR, NAEVE, NILSS
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resource that the property applies to, the object is another resource or an opaque
string saying something about the subject and the predicate is the type of the
property. A
dding properties to a resource can be done by anyone anywhere, i.e. the
properties do not need to be collected into one document or conceived of at the same
time as the document is created. Instead, the properties are spread out and there is no
easy way of

collecting them. This will present a challenge for the search engines of
tomorrow.

3.2.

What we can do with the Semantic Web in a learning context

Traditionally, a learning resource can be made accessible on the web and then
(possibly) updated by the provider.

Using a learning framework equipped with
semantic web techniques we may also do the following to a resource:


Describe it:
Since a resource can have uses outside the domains foreseen by the
provider, any given description (metadata instance) is bound to b
e incomplete.
Because of the distributed structure of RDF, a description can be expanded or new
descriptions following new formats (schemas) can be added. This allows for creative
uses of content in new and unforeseen ways. Hence, one of the most important

features of the current web
-

the fact that anyone can link anything to anything
-

has
been carried over into RDF.


Certify it:
There is no reason why only big organizations should certify learning
resources. Individuals, such as e.g. teachers, may want t
o certify certain content as a
quality learning resource that is well suited for specific learning tasks. Handling this
kind of certification will be an important part of the Semantic Web as pointed out in
[1]
.


Annotate it:
Ev
erything that has an identifier (URI) can be annotated. There are
already attempts under way in this direction. Annotea
[44]

is a project where
annotations in RDF format are created locally or on a server. The annotations apply

to HTML or XML documents and are automatically fetched and incorporated into
web pages via a special feature in the experimental browser Amaya
[45]
.


Extend it:
Structured content (typically in XML format) is becoming common.

Successive editing (diffs) can be done via special RDF
-
schemas allowing private,
group consensus or author
-
specific versions of a common base document. The
versioning history will be a tree with known and unknown branches, which can be
traversed with the
help of the next generation of versioning tools.


Use it everywhere:
RDF is application independent. Since the metadata is expressed
in a standard format, which is independent of the underlying schemas, even
simplistic applications can understand parts of
complex RDF graphs. If your favorite
tool does not support the corresponding schemas, it can at least present them in a
rough graph, table or whatever standard form it has for describing resources and their
properties. If more advanced processing software
is available (such as logic engines),
more advanced treatment of the RDF descriptions is possible.


And more:
Apart from these uses, you can invent new schemas describing
structures, personalization, results from monitoring and tracking, processes and
int
eractions that can enrich the learning environment in various ways.

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4.

APPLICATIONS AND SER
VICES
-

OUR CONTRIBUTIONS

The Semantic Web is a huge, interconnected, distributed graph that is not appropriate
for human consumption. We have proposed a structure cal
led the
Conceptual Web

[11]
, which will live on top of the Semantic Web and which will contribute to
making the latter accessible to humans in a more appealing and understandable way.
The Conceptual Web will be structured as a
Knowledge Manifold

[6]
,
[9]

and will be
explored through a
concept browser

[7]
,
[10]
. This is a new type of knowledge
management tool that l
ets you navigate different contexts by traversing the
contextual neighborhoods of each concept and view the content of a concept and/or
concept relation under a dynamically configurable set of aspects
[15]
.

4.1.

Conzilla

Conzilla

[12]
,
[13]

is a first prototype of a concept browser, which we have
developed within the KMR group at CID during the last 3 years
26
. Using a concept
browser, there are mainly two modes of exploration:


Browsing
is performed on prefabricated structures such as context maps, which
typically have a layout and a presentation that conforms to some visual language
such as UML
27
. Such maps typically represents a selection of what is relevant in a
certain contex
t. The linking between different contexts for the same concept is
performed similarly to how it is done in html. More advanced structures are also
available such as neighborhoods of maps and inspection of metadata. A concept
browser maintains a clear separ
ation of content from context and allows you to link
each concept to a set of content components that can be viewed in an external viewer
such as an ordinary web browser. The reader is referred to
[10]

for details.


Querying

is

performed when you do not find what you want in the prefabricated
structures and therefore need to actively search for knowledge. Both the query and
the results may be highly structured graphs that need user interfaces that are
appealing and easy to use.
With the help of our Learning Framework we plan to
extend the querying functionality of Conzilla beyond the basic filtering capabilities
that are available today
[15]
. This will be achieved through various forms of peer
-
to
-
peer

services that will be developed within the Edutella project described in section
4.2
.

4.2.

Edutella

RDF is a descriptive language and although it is distributed it needs
application/services in order to perform useful tasks. Actual
ly the tasks of finding
and distributing information require separate solutions in order to avoid well
-
known
access points such as search engines. These have a serious drawback, since the
information contained in the Semantic Web may change rapidly leaving

the search
engines of today far behind with their three months update
-
cycles
28
. The true power
of the Semantic Web will not be unleashed until there are ways to discover facts via



26

Conzilla is presently being develope
d as an open source project at SourceForge
[22]

and can be
downloaded from that site.

27

Unified Modeling Language
[16]

is an industry standard for object
-
oriented modeling. In fact, when
drawing context maps, we use our own modification of UML called ULM

(Unified Language
Modeling)
[10]
, which is more concept
-
oriented and has been especially designed in order to depict how
we speak about things.

28

S
ee
[19]

for a discussion on
wid
e respectively deep search.

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PALMÉR, NAEVE, NILSS
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queries that not only access remote repositories but also combine and logica
lly
process the results from several sources.

The KMR group at CID is participating in an international collaboration project
called PADLR
29
, whose driving vision is a learning web infrastructure which will
make it possible to exchange/annotate/organize an
d personalize/navigate/use/reuse
modular learning resources, supporting a variety of courses, disciplines and
universities. Within this project, we are collaborating with research groups at the
universities of Uppsala
[23]
, Sta
nford
[26]
, Hannover
[27]

and Karlsruhe
[28]

in
order to develop Edutella
[3]
,
[31]
, an infrastructure and a se
arch service for a peer
-
to
-
peer network that will facilitate the exchange of educational resources.

Edutella, which will be a set of services implemented within the JXTA system,
is aiming (among other things) to solve the search problems described above. T
he
envisioned services will include searching, mapping and replication. Searches will be
routed to anyone who has registered a matching answering capability. Mapping will
enable translation between schemas. This will allow very flexible reuse of
informatio
n, since an application will not need to adapt to competing or more
capable schemas because these schemas can be mapped to something that the
application already understands. There will be no closed formats. Replication will
allow metadata about learning r
esources to be spread across the web, which will
simplify the discovery of the corresponding resources.

4.3.

Virtual Workspace Environment

VWE is a distributed Learning Management System, which is designed to support
the construction of customizable learning en
vironments by enabling the composition
of learning resources
30
. It has many features in common with the "framework main
control" sketched in section
2.3
. In fact, VWE is a small configurable operating
system that can run in a we
b browser, which allows you to access your own learning
environment from everywhere.

5.

CONCLUSIONS AND FUTU
RE WORK

Learning, as well as other human activities, cannot be confined within well defined
boundaries such as course systems. In a broader perspective
, we want to enhance
existing and future environments and make them more interesting from a learning
perspective. As stated in
[9]

we believe that "nobody can teach you anything, a good
teacher can inspire you to learn". Hence
we acknowledge the crucial importance of
learner motivation. Moreover, the learning environment has to support trust building
and rich forms of communication between teacher and learner as well as between
learners
31
. In order to be powerful, the environment

must be inspiring and trigger
curiosity for the learning task
32
.




29

Personalized Access to Distributed Learning Resources
[30]
. This project is funded by the Wallenberg
Global Learning Network
[29]
.

30

V
WE has been developed under the coordination of Fredrik Paulsson of the KMR group at CID and the
Swedish National Agency for Education. He is also chair of the Swedish subcommittee of the ISO/IEC
standardization body for e
-
learning technology, ISO/IEC JTC1

SC36
[32]
.

31

An interesting experiment in this direction is described and analyzed in
[4]
.

32

A virtual reality based shared distributed 3D
-
environment called
Cyber
Math

[18]
, which aspires to
fullfil these requirements has been developed at CID. See
www.nada.kth.se/~gustavt/cybermath

for
quicktime movie demos of some

learning experiences in the CyberMath system.

E
-
LEARNING IN THE SEMA
NTIC AGE







11



In summary, we want to use the power and the flexibility of the Semantic Web
within our Learning Framework in order to develop tools, standards and
environments that support the following are
as:



Content management
, allowing dynamic creation of courses as well as distributed
annotations by both teachers and learners. The supporting technologies include:
content creation with distributed versioning capabilities, personal portfolios
33

and
perso
nal profiles.



Knowledge navigation
, focusing on the organization of ideas/concepts and their
relations in a conceptually clear context
34
. Separation of content and context allows
you to maintain an overview of the learning landscape. Navigation is carri
ed out
between contexts
35

that are connected through contextual neighborhoods. The
Conceptual Web
[11]

could form the basis for a global knowledge project, intended
to evolve and capture more and more of the accumulated human kn
owledge.



Experience
-
orientated environments
36
, where objects can be annotated by
anyone. If you want, you can share your annotations with other people
-

maybe
fellow learners, teachers or just people that are present for the experience. You can
communic
ate with them directly, launch a discussion or why not perform or engage
in some activity together.


The possibilities are limited only by your imagination.

REFERENCES

Papers and books


[1]

Berners
-
Lee, T.,
Semantic Web
Roadmap
,
www.w3.org/DesignIssues/Semantic.html
.


[2]

Berners
-
Lee, T. & Hendler, J. & Lassila, O.,
The Semantic Web
, Scientific
American, May 2001,
www.scien
tificamerican.com/2001/0501issue/0501berners
-
lee.html
.

[3]

Decker, S. & Manning, C. & Naeve, A. & Nejdl, W
.

& Risch, T. & Studer, R.
,
Edutella
-

An Infrastructure for the exchange of Educational Media
, Part of the
PADLR
[30]

propo
sal to WGLN
[29]
.

[4]

Knudsen, C. & Naeve, A.,
Presence Production in a Distributed Shared Virtual
Environment for Exploring Mathematics
, Proceedings of the 8
th

International
Conference on Advanced Computer Systems (ACS 2001), Szce
cin, Poland,

Oct. 17
-
19, 2001.

[5]

Melnik, S. & Decker, S.,
A Layered Approach to Information Modeling and
Interoperability on the Web
, Database group, Stanford Univdersity, Sept. 2000,

http://www
-
db.s
tanford.edu/~melnik/pub/sw00
.

[6]

Naeve, A.,
The Garden of Knowledge as a Knowledge Manifold
-

A Conceptual
Framework for Computer Supported Subjective Education
, CID
-
17, TRITA
-



33

The KMR group at CID, in collaboration with Uppsala Learning Lab
[24]
, have developed a special
portfolio system for this task.

34

This is (part of) the aspirati
on of a concept browser
[10]
, as exemplified by the Conzilla program
[22]
.

35

e.g.

visualized as context maps
expressed
in ULM

[10]
.

36

e.g.
shared distributed 3D
-
environments like

CyberMath.

12









PALMÉR, NAEVE, NILSS
ON


NA
-
D9708, Department of Numerical Analysis and Computing Science, KTH,
Stockholm,

1997,
http://cid.nada.kth.se/sv/pdf/cid_17.pdf
.

[7]

Naeve, A.,
Conceptual Navigation and Multiple Scale Narration in a Knowledge
Manifold,
CID
-
52, TRITA
-
NA
-
D9910, Department of Numerical Analysis and
Com
puting Science, KTH, 1999.
http://cid.nada.kth.se/sv/pdf/cid_52.pdf
.

[8]

Naeve, A.,
The Work of Ambjörn Naeve within the Field of Mathematics
Educational Reform
, CID
-
110, TRITA
-
NA
-
D0104, KTH, Stockholm, 2
001,
www.amt.kth.se/projekt/matemagi/mathemathics_educational_reform.doc
.

[9]

Na
eve, A.,
The Knowledge Manifold
-

an Educational Architecture that supports
Inquiry
-
Based Customizable Forms of E
-
Learning
, Proceedings of the 2
nd

European Web
-
Based Learning Environment Conference (WBLE 2001), Lund,
Sweden,

Oct. 24
-
26, 2001.

[10]

Naeve, A.,
T
he Concept Browser
-

a New Form of Knowledge Management
Tool
, Proceedings of the 2
nd

European Web
-
Based Learning Environment
Conference (WBLE 2001), Lund, Sweden, Oct. 24
-
26, 2001.

[11]

Naeve, A. & Nilsson, M. & Palmér, M.,
The Conceptual Web
-

our research
vis
ion
, Proceedings of the first Semantic Web Working Symposium, Stanford,
July 2001,
www.semanticweb.org/SWWS/program/position/soi
-
nilsson.pdf
.

[12]

Nilsson, M. & Palmér, M.,
Conzilla

-

Towards a Concept Browser
, CID
-
53,
TRITA
-
NA
-
D9911, KTH, 1999,
http://cid.nada.kth.se/sv/pdf/cid_53.pdf
.

[13]

Nilsson, M.,
The Conzilla Design
-

The definitive reference
, CID/NADA/KTH,
2000,
http://conzilla.sourceforge.net/doc/conzilla
-
design/conzilla
-
design.html
.

[14]

Nilsson, M. (ed.),
IMS/LOM
-
RDF binding
,
www.imsproj
ect.org/rdf/index.html
.

[15]

Pettersson, D.,
Aspect Filtering as a Tool to Support Conceptual Exploration
and Presentation
, TRITA
-
NA
-
E0079, CID/NADA/KTH, Dec. 2000.

[16]

Rumbaugh, J. & Jacobsson, I. & Booch, G.,
The Unified Modeling Language
Reference Manual
, Addis
on Wesley Longman Inc., 1999.

[17]

Tanenbaum, A. S.,
Computer Networks
, Prentice
-
Hall, 3rd ed., 1997.

[18]

Taxén G. & Naeve, A.,
CyberMath
-

Exploring Open Issues in VR
-
based
Learning
, SIGGRAPH 2001 Educators Program, In SIGGRAPH 2001
Conference Abstracts and Applic
ations, pp. 49
-
51,
www.nada.kth.se/~gustavt/cybermath
.

[19]

Waterhouse, S
., JXTA Search: Distributed Search for Distributed Networks
,

http://search.jxt
a.org/JXTAsearch.pdf
.


[20]

Wilson, S.,
The next big thing?
-

Three architectural frameworks for learning
technologies
, CETIS,
www.cetis.ac.uk/content/20010828163808/viewArticle
.


Relevan
t web sites


[21]

CID (Centre for user
-
oriented IT Design):
http://cid.nada.kth.se/il
.

[22]

Conzilla development:
http://conzilla.sourceforge.net
.

[23]

UDBL (Uppsala Data Base Laborat
ory):
www.dis.uu.se/~udbl
.

[24]

ULL (Uppsala Learning Lab):
www.ull.uu.se
.

[25]

SweLL (Swedish Learning Lab):
www.swedishlearninglab.org
.

[26]

S
NLPG (Stanford Natural Language Processing Group):
http://nlp.stanford.edu
.

[27]

KBS
-
Hannover:
www.kbs.uni
-
hannover.de
.

[28]

AIFB
-
Karlsruhe:
www.aifb.uni
-
karlsruhe.de/Forschungsgruppen/index_en.html
.

[29]

WGLN (Wallenberg Global Learning Network):
www.wgln.org
.

[30]

PADLR (Personalized Access to Distributed Learning Repositories
) proposal to
WGLN, Granted March 2001:
www.learninglab.de/pdf/L3S_padlr_17.pdf
.

[31]

Edutella:
http://edutella.jxta.org
.

E
-
LEARNING IN THE SEMA
NTIC AGE







13



[32]

ISO/IEC JTC1 SC36:
http://jtc1sc36.org
.

[33]

IEEE (Institute of Electrical and Electronics Engineers):
http://standards.ieee.org
.


[34]

IMS (Instructional Management Systems):
www.imsproj
ect.org
.

[35]

SCORM (Sharable Content Object Model) Reference Model 1.1, ADL,
Department of Defense, USA, Jan. 2001,
www.adlnet.org/scorm/scorm.cfm
.


[36]

ADL (Advanced Distributed Learning):
www.adlnet.org
.

[37]

ARIADNE (Alliance of Remote Instructional Authoring and Distribution
Networks for Europe):
http://ariadne.unil.ch
.


[38]

AICC (Aviations Industry CBT Committee):
www.aicc.org
.

[39]

RDF (Resource Description Framework):
www.w3.org/RDF
.

[40]

RDF Schema:
www.w3.org/TR/2000/CR
-
rdf
-
schema
-
20000327
.


[41]

OIL (Ontology Inference

Layer):
www.ontoknowledge.org/oil
.


[42]

JXTA (The Juxtapose project):
www.jxta.org
.

[43]

Semantic Web initiative:
www.SemanticWeb.org
.

[44]

Anno
tea:
www.w3.org/2001/Annotea
.


[45]

Amaya:
www.w3.org/Amaya
.


[46]

OKI (Open Knowledge Initiative):
http://web.mit.edu/oki
.


[47]

WSDL (Web Services

Description Language):
www.w3.org/TR/wsdl
.


[48]

Blackboard:
www.blackboard.com
.